scholarly journals Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Kun Wang ◽  
Yi Cheng ◽  
Ke Song
Keyword(s):  
Deep Learning ◽  
Working Conditions ◽  
Health Management ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Key Technology ◽  
Life Estimation ◽  
Useful Life ◽  
New Generation ◽  
Deep Learning Model

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

scholarly journals A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Zhiyuan Xie ◽  
Shichang Du ◽  
Jun Lv ◽  
Yafei Deng ◽  
Shiyao Jia
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Recurrent Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Second Phase ◽  
Learning Methods ◽  
Useful Life ◽  
Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

scholarly journals Physics Based Deep Learning Technique for Prognostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Khaled Akkad
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Health Management ◽  
Recognition Performance ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Learning Technique ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

scholarly journals A data-driven framework for remaining useful life estimation

2017 ◽  
Vol 55 (5) ◽  
pp. 557 ◽  
Author(s):  
Hoa Dinh Nguyen
Keyword(s):  
Data Processing ◽  
Health Management ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Life Estimation ◽  
Search Experiment ◽  
Useful Life ◽  
Structural Health Management ◽  
Processing Techniques ◽  
Data Library

Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete  run of the system, the RUL can be predicted  using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and  in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches  are effective in performing RUL prediction.

scholarly journals A novel deep capsule neural network for remaining useful life estimation

2019 ◽  
Vol 234 (1) ◽  
pp. 151-167 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett ◽  
Rodrigo Pascual
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computation Time ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fault Diagnostics ◽  
Data Set ◽  
Life Estimation ◽  
Useful Life ◽  
Fault Prognostics

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

scholarly journals Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878420 ◽  
Author(s):  
Shu-Fa Yan ◽  
Biao Ma ◽  
Chang-Song Zheng
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Cumulative Distribution ◽  
Kernel Principal Component Analysis ◽  
Degradation Index ◽  
Life Estimation ◽  
Power Shift ◽  
Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

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